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Automated quality classification of colour fundus images based on a modified residual dense block network

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Abstract

Fundus image-assisted diagnosis has become an intuitive and standard diagnostic technique in recent years. In hospital treatment and telemedicine, experts conduct pathological analysis and treat patients with fundus diseases using retinal images that are captured by fundus cameras. The quality of fundus images is therefore crucial for doctors to provide timely and accurate diagnoses of the diseases. However, the different experiences of fundus photographers result in different image qualities, and some images are marked as unreadable by the diagnostician, seriously delaying the treatment of patients. To solve the quality classification problem, a modified residual dense block convolution neural network (MRDB-CNN) is proposed in this paper. The two categories of images, “good quality” and “poor quality”, are used as the training data set and testing data set to train the CNNs and verify the classification results. The experimental results show that, compared with other existing network structures, the network proposed in this paper can classify the “good-quality” and “poor-quality” fundus images more accurately with an accuracy rate of 99.90%. In addition, the tests for the “ambiguous-quality” category further demonstrate that our method can obtain more detailed features of the fundus images and give an objective score to the image quality. In short, the MRDB-CNN can accurately classify the quality of fundus images while avoiding the complex preprocessing of traditional algorithms, and it satisfies the needs of hospital treatment and telemedicine for the real-time quality judgement of fundus images.

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Acknowledgements

This work was supported by Tianjin Science and Technology Major Projects and Engineering (Nos. 17ZXSCSY00060, 17ZXHLSY00040) and the Program for Innovative Research Team in University of Tianjin (Grant No. TD13-5034).

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin, 300387, China

    Fang Zhang, Xu Xu, Zhitao Xiao, Jun Wu, Lei Geng, Wen Wang & Yanbei Liu

  2. Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin, 300387, China

    Fang Zhang, Xu Xu, Zhitao Xiao, Jun Wu, Lei Geng, Wen Wang & Yanbei Liu

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  1. Fang Zhang
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  2. Xu Xu
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  3. Zhitao Xiao
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  6. Wen Wang
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Correspondence to Zhitao Xiao.

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Zhang, F., Xu, X., Xiao, Z. et al. Automated quality classification of colour fundus images based on a modified residual dense block network. SIViP 14, 215–223 (2020). https://doi.org/10.1007/s11760-019-01544-y

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